EP2599039B1 - Method and system for flight data anlysis - Google Patents

Description

The present invention relates to a method and a system for analyzing a set of flight data whose values have been recorded during a flight of an aircraft.

One of the main causes of aircraft delays is related to technical problems occurring on these aircraft in an impromptu manner. In addition, the maintenance phases of these aircraft requiring their prolonged immobilization are very often restricted to the minimum for reasons of profitability.

The regulation in terms of maintenance and air traffic defines standards that airlines must respect in order to ensure a user a maximum level of security.

However, it has been observed that in recent years, flight safety is stagnating. In order to continue to increase flight safety despite the continuous increase in air traffic and to optimize maintenance phases, airlines have been equipped with flight data analysis systems.

These systems allow airlines to understand in detail the progress of a flight from regular recordings of the values of these flight data made during each flight of each of their aircraft.

For this, these systems detect singular events occurring during the flight. An expert then analyzes these events which indicate that a technical incident occurred during the flight, that a practice or a condition provided for by a flight procedure was not respected, thus preventing at a very early stage of possible incidents or accidents that could occur.

The Fig. 1 schematically represents a system known as FDM (Flight Data Monitoring in English) which makes it possible to analyze a set of flight data { p ( i )} _{i ∈ I} with I ⊂ D and D the set of data indices of flight, whose values { v ( i )} _{i ∈ I} are recorded during a flight of an aircraft.

The principle of the system consists in equipping the aircraft with means for recording these values { v ( i )} _{i ∈ I.} These means are, for example, a black box or specific recorders such as ACMS (Aircraft Condition Monitoring System). These flight data { p ( i )} _{i ∈ I} (and therefore their values) are in multiple forms. For example, a value of a flight data item may be a possibly multidimensional measurement of flight parameter (fluid pressure, air speed, vibration frequency, etc.) or represent an occurrence of a flight parameter. action performed by the flight crew (activation of the autopilot etc.).

When the aircraft has completed its flight or when it is in stopover at an airport, the values { p ( i )} _{i ∈ I} are recovered by a processing unit T via wired or wireless communication means and analyzed by a data mining algorithm, better known as Data Mining.

For this purpose, an expert determines and records previously in a database BDD flight data sets { p ( j )} _{j ∈ J} with J ⊂ D and a set of flight events E _{J, k} relating to each flight set { p ( j )} _{j ∈ J.} Each flight event E _{J, k} relative to a set of J flight data { p ( j )} _{j ∈ J} corresponds to the values { v _k ( j )} _{j ∈ J} of this set which have been recorded during a previous flight (k designates, for example, an order of registration in the database of the values { v _k ( i )} _{j ∈ J} ) and under the constraint that the value v _k ( m ) of at least one of these flight data { p ( m )} _{m ∈ J} exceeds its nominal value L _m which is also prerecorded and is established to comply with current aviation safety regulations and flight procedures specific to each airline. In mathematical terms, E _{J, k} = { v _k ( j ) _{j ∈ J} | v _k ( j )> L _j ≠ ∅} or that E _{J, k} is the non-empty set of { v _k ( j )} _{j ∈ J} such that v _k ( j ) is greater than the nominal value threshold L _j , i.e. there is at least one flight data value that is not nominal. This implies that there exists a relation of order associated with the data j which makes it possible to write v _k ( j )> L _j . This relationship may be different for each flight data and is not necessarily defined by the comparison operator of two integers.

Once the expert has determined and memorized these different sets of flight data and the flight events associated with them, he defines means, usually in the form of computer programs, so that these events E _{J, k} are detected following the recording during a flight of an airplane of the values { v ( i )} _{i ∈ I} of a set of flight data { p ( i )} _{i ∈ I.} The context in which a flight event has occurred can also be recorded. For example, the context may be the name of the sensor that has recorded flight data, or the location of the aircraft at the time of this recording, etc.

Using these means, the algorithm then detects an event E _{J, k} if there is at least one value { v ( i )} _{i ∈ I} of a flight data set of the flight data set { p ( i )} _{i ∈ I} that exceeds a nominal value and that this flight data corresponds to a value { v _k ( j )} _{j ∈ J} of a flight data item of a flight data set { p ( j )} _{j ∈ J} previously saved in the database BDD. It is thus necessary to find J and k such that I = J with I associated with a known singular flight event and k an occurrence of E _{J, k} in the recording of the data carried out so far.

The processing unit T also usually calculates statistics on the values of the sets of flight data recorded during several flights of the same aircraft and / or on the various flight events that have been detected. The processing unit T then derives trends concerning the evolution over time of these flight data and / or flight events. These trends evaluate whether a risk increases or decreases. For example, a value of flight data recorded during a flight may also be recorded in future flights and at the end of a flight. number of flights, a diagram of the temporal evolution of this flight data can be established.

The system also includes graphical GUI means to present to an expert these different flight events and other trends. These representations sometimes take the form of tables in which are given the data of a flight or several flights, the exceedances of the nominal values and other temporal diagrams representing the trends of flight data and other evolutions of flight events.

The expert then analyzes these representations to validate whether the flight events detected following the analysis of the values { v ( i )} _{i ∈ I} correspond to a flight incident to accurately trace the flight and / or the evolution of trends.

He then deduces relationships between flight events that occurred during a flight or several flights of an aircraft and therefore provides an action to be taken to prevent these flight events from happening again or that a major incident forces me to stop the plane. It can for example provide for a possible maintenance phase of the aircraft and / or a possible modification in a flight procedure and / or specific training of the train crew. For this purpose, the graphical means GUI allow the expert to manually modify or enter new sets of flight data to be recorded and / or new flight events and / or new nominal values.

The airlines have equipped their aircraft with a multitude of sensors in order to record a maximum of flight data, considering that the more the expert will have at his disposal a large number of flight data values and more harmless situations will be recorded. in the database, ie more flight events will be detected, the expert will be able to accurately assess the flight situations that led to these events and thus predict ahead of time a greater number of major incidents or future accidents.

In addition, aviation safety regulations are changing (consequences of accidents, energy or economic efficiency, taking into account the increase in traffic, etc.) and regularly requires the consideration of new flight events. This results in an evolution of both the recording means in flight and means for detecting these new flight events. Likewise, an airline can add or modify its own events to further improve flight safety and optimize its procedures.

Although, in theory, the multiplication of flight data recordings and flight data sets to be recorded makes it possible to increase the accuracy and relevance of an expertise of flight situations, in practice the systematic analysis of flight situations expert in all detected flight events mobilizes significant resources, both in terms of computing power necessary for the analysis of the flight data values by the processing unit T and in terms of workload for the expert. Generally, only flight data sets and other flight events imposed by aviation safety regulations and airline procedures are analyzed.

These experts therefore only make their assessments of flight situations from a very limited number of flight events that they pre-recorded in the BDD database and which represent only a small part of the flight events. which could be detected from the values of the flight data recorded during a flight of an airplane. However, some of these "unregistered" flight events, which appear to be minor in the eyes of the expert, could allow the detection of underlying future problems that do not appear in their expertise reports because of their non-systematic analysis of all recorded flight data values.

The current analysis of a set of flight data recorded during a flight of an aircraft therefore allows an expert to relate flight events and / or trends for identical flight conditions (same aircraft , same flight plan) while it would be advantageous for the expert to also have access to all the detectable flight events from the recorded flight data values, that is to say, among other things to flight events that occurred under flight conditions similar to those in which a flight event recorded in the BDD database has already occurred.

Such a system would then be able to enrich the database BDD flight data recorded by the various aircraft of an airline, or even several airlines increasing the probability that a flight event has already occurred and therefore increase its detectability. Such a system would thus have the possibility of enriching and improving the detections of new flight events not foreseen by the expert.

Such a flight data analysis system would then allow a significant reduction in the workload of the expert who would no longer have to cross-check the flight conditions to determine if flight conditions are similar, and an airline would have then a vision of the risks incurred by all the aircraft in his fleet without the expert having a significant increase in his workload.

Another disadvantage of using current data mining algorithms is that flight events, newly determined by an expert, are recorded manually by this expert using GUI graphical means. It would be very advantageous for the system to automatically generate new flight events or even new sets of flight data as a result of its own analysis of the recorded flight data values. The intervention of an expert would then be required only to validate any conflicts between the events and / or sets of flight data.

The problem solved by the present invention is to determine a flight data analysis system that overcomes the disadvantages raised.

For this purpose, in general, the present invention relates to a method for analyzing a set of flight data, said first, whose values were recorded during a flight of an aircraft and in which an event is detected when the values of the flight data of the first set and the flight data values of another set of flight data, said second, which have been recorded during a previous flight are values relative to the same flight data and if the same values of the flight data of the first set and the second set exceed their respective nominal values.

According to one characteristic, at least one subset of flight data, comprising at least one of the flight data of the first set and / or at least one flight data of a second set under the condition that at least one of its data exceeds its nominal value, is defined by correlating the value of at least one flight data item of the first set with the value of at least one of the flight data of the second set. In addition, a flight event is then likely to be detected from the values of the flight data of a subset and the flight data values of one of the second sets of flight data, and if a flight event is not detected from a subset, a probability of matching between at least a second set and this subset is associated to this subset, the first set is then updated by adding at least one new flight data of at least a second set and / or by deleting at least one of its flight data and this, according to the match probability values. The first set of flight data thus updated is then recorded again during a new flight, and the process is iterated as long as a maintenance or flight safety expert can not make a decision on this sub-flight. together.

This method enriches the initial knowledge of a database consisting of a predefined set of flight events that may occur.

Indeed, thanks to the automatic determination of correlations between the previously recorded flight data values and the flight data values to be analyzed, new sets of flight data (or subsets) are generated by the process which leads to the possible detection of new flight events that an expert had not initially planned.

According to one variant, each subset is presented to an expert. If the expert decides that a subset is preponderant, this subset is stored, and if he decides that a subset is a false alarm, this subset is no longer considered.

This subassembly presentation allows an expert to decide the relevance of the flight events related to these subsets so that only flight events that are relevant to the safety of future flights are added to the system. or relating to the evolution of an airline's flight procedure.

According to one embodiment, at least one new flight data item that is added to the first set comes from the second set which maximizes the probability of matching with a subset.

This mode is advantageous because it optimizes the chances that the expert can decide on the relevance of a subset from the next record of the new set of flight data thus modified.

According to one embodiment, the pairing probabilities are recorded and updated during each iteration of the method, and the second sets, from which is obtained said at least one new flight data for updating the first set. , are selected from the variation in time of their probabilities of pairing.

Thus, the analysis of the values of the flight data of the first set makes it possible to identify the variation of the sets of flight data recorded during several successive flights and this to take into account the evolutions of the flight conditions but also the possible modifications. flight procedures and / or aviation safety regulations that are introduced by an expert.

According to one variant, the flight event detection is also updated when the first set is updated so that this detection can detect a flight event relating to said subset.

This variant is advantageous because it allows flight events defined from subsets to be detected in the future, even if these events could not be hitherto.

According to a variant of this variant, simulated flight data are used to validate the modifications of the event detection thus modified.

These flight data make it possible to validate the operation of the system once the detection of events has been updated, that is to say, among other things, to validate that the events previously detected are still so, that false results do not occur. do not occur in relation to the nominal values, or that the limit values are reached, etc.

The present invention also relates to an analysis system which comprises means for implementing one of the above methods.

• La Fig. 1 représente schématiquement un système d'analyse d'un ensemble de données de vol dont les valeurs ont été enregistrées au cours d'un vol d'un avion, et
• La Fig. 2 représente schématiquement un système d'analyse d'un ensemble de données de vol {p(i)} _i∈I dont les valeurs ont été enregistrées au cours d'un vol d'un avion selon la présente invention.

The characteristics of the invention mentioned above, as well as others, will appear more clearly on reading the following description of an exemplary embodiment, said description being given in relation to the attached drawings, among which:

• The Fig. 1 schematically represents a system for analyzing a set of flight data whose values have been recorded during a flight of an airplane, and
• The Fig. 2 schematically represents a system for analyzing a set of flight data { p ( i )} _{i ∈ I} whose values have been recorded during a flight of an aircraft according to the present invention.

The references of the Fig. 2 which are identical to those of the Fig. 1 represent the same elements.

The analysis system of the Fig. 2 comprises a processing unit T which groups means for detecting a flight event (E _{J, k} ) when the values of the flight data of the set { p ( i )} _{i ∈ I} and the values { v _k ( j }} _{j ∈ J} flight data of a set of flight data { p ( j )} _{j ∈ J} , which have been recorded during a previous flight, are values relating to the same flight data and, if the same values v _k ( m ) _{m ∈ J} flight data of the set { p ( i )} _{i ∈ I} and the set { p ( j )} _{j ∈ J} exceed their respective nominal values.

In practice, the database BDD has several sets { p ( j )} _{j ∈ J.}

The processing unit T also comprises means for defining at least one subset of flight data { p ( o )} _{o ∈ O} which comprises at least one of the flight data of the set { p ( i )} _{i ∈ I} and / or at least one flight data of a set { p ( j )} _{j ∈ J} under the condition that at least one of its data exceeds its nominal value, by correlation of the value { v ( i )} _{i ∈ I} of at least one flight data of the set { p ( i )} _{i ∈ I} with the value { v _k ( j )} _{j ∈ J} of at least one of the flight data of the set { p ( j )} _{j ∈ J.}

The processing unit T also comprises means for determining and associating with each subset { p ( o )} _{o ∈ O} a probability of matching Pr ( p _o | p _j ) between at least one set { p ( j )} _{j ∈ J} and this subset { p ( o )} _{o ∈ O} , and means for updating the set { p ( i )} _{i ∈ I} by adding at least one new flight data { p ( n )} _{n ∈ N} ( N ⊂ D ) of at least one set { p ( j )} _{j ∈ J} and / or by deleting at least one of its flight data { p ( i )} _{i ∈ I} and this, according to the probability values of matching Pr ( p _j | p _o ).

The means of the processing unit T are implemented, according to one embodiment, in the form of computer programs.

The analysis method, implemented by such a processing unit T, makes it possible to analyze the values of the set of flight data { p ( i )} _{i ∈ I} which is not necessarily known in advance from the database BDD. Thus, it makes it possible to minimize the task of an expert to relate new flight events from this set of flight data to be analyzed with the flight events E _{J, k} previously recorded in the database BDD.

This process is iterative and implements a method that can be likened to an analysis that is multidimensional and parameterized by unsupervised learning.

First, at least one subset of flight data, comprising at least one of the flight data of the set { p ( i )} _{i ∈ I} and / or at least one flight data of a set { p ( j )} _{i ∈ I} provided that at least one of its data exceeds its nominal value, is defined by correlation of the value { v ( i )} _{i ∈ I} of at least one flight data of the set { p ( i )} _{i ∈ I} with the value { v _k ( j )} _{j ∈ J} of at least one of the flight data of a set { p ( j )} _{j ∈ J.}

The sets I and J are arbitrary sets of the set D of all the flight data and no hypothesis of relationship between them exists. Thus, the set I and J can have an empty or non-empty intersection, I (respectively J) can be included in J (respectively I).

The definition of the correlation between values of the flight data of the set { p ( i )} _{i ∈ I} and values of the flight data of the set { p ( i )} _{j ∈ J} is within the range of the skilled person. For example, a set of rules can be preprogrammed so that some flight data systematically form a subset as soon as they are present in both sets, and others can be discarded as soon as they occur in either one or the other. other set. Rules can also be established to quantify the correlation between values and form subsets only with flight data that have high correlation values.

Once one or more subsets have thus been formed, a flight event E _{J, k} is then likely to be detected from the values of the flight data { v ( o )} _{o ∈ O} of a sub-set. set { p ( o )} _{o ∈ O} and the values { v _k ( j )} _{j ∈ J} of the flight data of one of the sets of flight data { p ( j )} _{j ∈ J.} We recall that an event E _{J, k} is detected if { v ( o )} _{o ∈ O} and { v _k ( j )} _{j ∈ J} of a set { p ( j )} _{j ∈ J} are relative values the same flight data (I = J in particular) and if { v ( o ) _{o ∈ O} and { v _k ( j )} _{j ∈ J} exceed their respective nominal values.

This case corresponds to the detection of a flight event that has already occurred during a previous flight.

If, on the other hand, a previous flight event is not detected from a subset, then a probability of matching Pr ( p _o | p _j ) between at least one set { p ( j )} _{j ∈ I} and this subset is associated with this subset.

avec y une estimation de la classification de tous les sous-ensembles p_o dans les classes pj.According to one embodiment, the estimation of these probabilities Pr ( p _o | p _j ) can be seen as a direct problem of classification of each subset from the set { p ( i )} _{i ∈ I.} Considering each set { p ( j )} _{j ∈ J} as event classes that group together the flight events that are detected by these sets of flight data, and that the set { p ( i )} _{i ∈ I} consists of a plurality of independent subsets, the direct problem for classifying the subsets is expressed by $$\mathrm{there}=\mathrm{arg\; max}{\displaystyle \underset{o=1}{\overset{O}{\Pi }}P\left(p_o|p_j\right)}$$

with y an estimate of the classification of all subsets p _o in classes pj.

It is known to solve this problem to use a so-called Cauchy / Naive / Bayes classification method which has been widely used in the field of classification of image primitives ( Emotion Recognition Using a Cauchy Naive Bayes Classifier, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (6): 636-642, 1996 ).

This method is iterative and allows to converge to a classification of subsets that is stable. At that time, each of the probabilities Pr ( p _o | p _j ) is set to a value which is high when the subset and the set p _o have a large number of flight data in common.

The set { p ( i )} _{i ∈ I} to be analyzed is then updated by adding at least one new flight data { p ( n )} _{n ∈ N} of at least one set { p ( j ) } _{j ∈ J} and / or by deleting at least one of its flight data { p ( i )} _{i ∈ I} , and this according to the matching probability values Pr ( p _j | p _o ).

Thus, since the probability of matching quantifies the resemblance between the set { p ( j )} _{j ∈ J} and each subset, the N flight data { p ( n )} _{n ∈ N} of the set { p ( j )} _{j ∈ J} chosen can be added to the set { p ( i )} _{i ∈ I} which will allow in future recordings of these flight data values { p ( j )} _{j ∈ I∪J} of provide guidance that allows an expert to make a decision as to what to do with the subassembly.

In the case where several subsets would be formed, at the end of the calculation of the probabilities, the N flight data { p ( n )} _{n ∈ N} added depend on the results of the set of probabilities.

The flight data set thus updated { p ( j )} _{j ∈ I ∪ J} is then recorded again during a new flight, and the process is iterated until an expert can make a decision. on this subset.

The addition of new flight data is a kind of dynamic flight recorder programming that maximizes the probability of detecting new events but also the probability of detecting an event long before a problem or failure occurs. occurs during the flight of an airplane.

According to a variant, each subset is presented to an expert via GUI graphical means. The expert can then decide if this subset is a preponderant flying event. If this is the case, this subset is stored in the database BDD and then enriches the system of this new flight event. If it decides that a subset is a false alarm, that subset is no longer considered.

According to one embodiment, at least one new flight data { p ( n )} _{n ∈ N} which is added to the set { p ( i )} _{i ∈ I} is derived from the set { p ( j )} _{j ∈ J} that maximizes the probability of matching with a subset.

According to one embodiment, the probabilities of pairing Pr ( p _j | p _o ) are recorded and updated at each iteration of the method, and the sets { p ( j )} _{j ∈ J} , from which is obtained said at least one new flight data { p ( n )} _{n ∈ N} for updating the set { p ( i )} _{i ∈ I} , are selected from the variation in time of their probabilities of pairing.

Alternatively, the detection of theft event is also updated when the set {p (i)} _{i ∈ I} is updated so that the detecting means can detect a relative flight event, a sub together.

Advantageously, simulated flight data are used to validate the modifications of the modified event detection.

Claims (7)

1. Method for the analysis, by a processing device, of a first set of flight data that are related to the functioning of an aircraft when it is on flight ({p(i)} _i∈I ) the values ({v(i)} _i∈I ) of which were recorded during a flight, second sets of flight data that were recorded during previous flights being stored in a database and one nominal value was recorded beforehand for each flight data flights , the method being such that a flight event (E_J,k) is detected if there is at least one value {v(i) _i∈I of one flight data of the first set of flight data exceed its nominal value and that this flight data corresponds to a value {v_k (j) _j∈J of one flight data of one of said second sets of flight data {p(j)} _j∈J beforehand stored,
   characterised in that said method comprises the following steps:

   - a step of defining a subset of flight data ({p(o)} _o∈O ) that comprises at least one of the flight data of the first set ({p(i)} _i∈I ) and at least one flight data item of one of said second sets ({p(j)} _j∈J ), at least one of which exceeds its nominal value, at least one value of which exceeds its nominal value a correlation of this or these flight data ({v(i) _i∈I ) of this or these flight data of said first set ({p(i)} _i∈I ) with the value ({v_k (j)} _j∈J ) of this or these flight data of said second set ({p(j)} _j∈J ) existing,

   - step of detecting an event of flight (E_J,k) when at least one value of one flight data of said subset of flight data ({p(o)} _o∈O ) exceeds a nominal value and when this flight data corresponds to one value {v_k (j)} _j∈J of one flight data of one of said second sets of flight data {p(j)} _j∈J beforehand stored, and

   - if no flight event is not detected at previous step, the following steps are performed,

   - a step of associating to said subset of flight data {p(o)} _o∈O a probability value of pairing (Pr(p_o |p_j )) between at least one of said second sets of flight data ({p(j)} _j∈J ) and said subset of flight data ({p(o)} _o∈O )

   - a step of updating of said first set of flight data ({p(i)} _i∈I ) according to the probability value of pairing associated to said subset of flight data, said step of of updating comprising:

   - a step of adding at least one new flight data item ({p(n)} _n∈N ) of at least one of said second sets of flight data ({p(j)} _j∈J ) to the first set of flight data and/or

   - a step of deleting at least one of the flight data ({p(i)} _i∈I ) of the first set of flight data ({p(i)} _i∈I );

   - a step of presenting said subset via a graphical interface.
2. Method according to claim 1, in which said subset is presented to an expert via a graphical interface to enable the expert to decide whether said subset is preponderant and must be stored by the processing device or whether it corresponds to a false alarm and must not be considered further.
3. Method according to claim 1 or 2, in which at least one new flight data item ({p(n)} _n∈N ) that is added to the first set of flight data ({p(i)} _i∈I ) comes from the set among said second sets of flight data ({p(j)} _j∈J ) that maximises the pairing probability with said subset ({p(o)} _o∈O ).
4. Method according to one of claims 1 to 3, in which the pairing probability value or values (Pr(p_j |p_o )) are recorded and updated at each iteration of the method, and the second sets of flight data ({p(j)} _j∈J ), from which said at least one new flight data item ({p(n)} _n∈N ) is obtained for updating the first set of flight data ({p(i)} _i∈I ), are selected using the variation over time of their pairing probabilities.
5. Method according to one of claims 1 to 4, in which the flight event detection is updated when the first set of flight data is updated so that this detection can detect a flight event relating to said subset of flight data.
6. Method according to claim 5, in which the simulated flight data are used to validate the updating of the event detection.
7. System for the analysis, by a processing device, of a first set of flight data that are related to the functioning of an aircraft when it is on flight ({p(i)} _i∈I ) the values ({v(i)} _i∈I ) of which were recorded during a flight, said system comprising a database (BDD) in which second sets of flight data ({p(j)} _j∈J ) recorded during the previous flights are stored, means for memorizing a nominal value for each flight data, and means for detecting a flight event (E_J,k) if there is at least one value {v(i)} _i∈I of one flight data of the first set of flight data exceed its nominal value and that this flight data corresponds to a value {v_k (j)} _j∈J of one flight data of one of said second sets of flight data {p(j)} _j∈J beforehand stored
   characterised in that it also comprises:

   - means for defining a subset of flight data ({p(o)} _o∈O ), so that it comprises at least one of the flight data of the first set of flight data ({p(i)} _i∈I ) and at least one flight data item of one of said second sets of flight data ({p(j)} _j∈J ), at least one of which exceeds its nominal value, and so that there exists a correlation of the value ({v(i)} _i∈I ) of this or these flight data of said first set ({p(i)} _i∈I ) with the value ({v_k (j)} _j∈J ) of this or these flight data of said second set ({p(j)} _j∈J ), and

   - means for determining, and associating with said subset of flight data ({p(o)} _o∈O ), a probability value of pairing (Pr(p_o |p_j )) between at least one of said second sets of flight data ({p(j)} _j∈J ) and this subset of flight data ({p(o)} _o∈O ), and

   - means for updating the first set of flight data ({p(i)} _i∈I ) according to the probability value of pairing associated to said subset of flight data, said updating consisting of adding at least one new flight data item ({p(n)} _n∈N ) of at least one of said second sets of flight data ({p(j)} _j∈J ) to the first set of flight data and/or deleting at least one of the flight data ({p(i)} _i∈I ) of the first set of flight data ({p(i)} _i∈I );

   - means for presenting said subset via a graphical interface.

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